ArticleThe journal of physical chemistry letters2025
Architecture-Independent Absolute Solvation Free Energy Calculations with Neural Network Potentials.
Article in The journal of physical chemistry letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Predicting Solvation Free Energies of Molecules and Ions via First-Principles and Machine-Learning Molecular Dynamics.Journal of chemical theory and computation · 2026Article
- Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.Journal of chemical theory and computation · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Allowing atoms or molecules to disappear is a critical step in alchemical free energy simulations (FES). The necessary tricks are well understood when using force fields. Over the past few years, neural network potentials (NNPs) have seen rapid development. Their potentially higher accuracy compared to force fields makes them attractive for use in FES. Here, we outline a method for gradually decoupling atoms and molecules in systems that are fully described by NNPs. Specifically, we show that manipulating the neighbor list is equivalent to using soft-core potentials in force-field-based FES. Since constructing the neighbor list is a central step, regardless of the NNP's inner workings, our approach is agnostic to NNP architecture. We validate the correctness of our methodology by demonstrating cycle closure for a model problem and report solvation free energies obtained with the MACE-OFF23(S/M) NNP.
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Registered trials
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